BANTER for Beaked Whales: EPacific (CCES survey)

###Organizational Instructions Save this file in an R Project that includes the following foldering: /Project |-Database/ |-Binaries/ |-Figures/ |-epacific |_ beakerBanter_epacific.Rmd |_ epacific_banter_data.rds |_ epacific_banter_data_ici.rds |_ epacific_banter_model_ec_t1e4s3_t1e4s4.rds |_ epacific_banter_model_ici_t1e4s3_t1e4s4

PAMpal Data Processing

Start by loading the required packages

library("easypackages")

## Warning: package 'easypackages' was built under R version 4.2.3

libraries("PAMpal", "banter", "rfPermute", "kableExtra", "magick", "magrittr", "here")

## Warning: package 'dplyr' was built under R version 4.2.3

## Warning: package 'banter' was built under R version 4.2.3

## Warning: package 'magick' was built under R version 4.2.3

here()

## [1] "C:/Users/shannon.rankin/Documents/GitHub/BANTER_BeakedWhales"

1. Set up our PPS (PAMPal Settings Object) for the EPacific dataset

pps <- PAMpalSettings(db='Databases/', 
                      binaries = 'Binaries/',
                      sr_hz='auto', 
                      winLen_sec=.0025, 
                      filterfrom_khz=10, 
                      filterto_khz=NULL)

2. Process data and save to file to eliminate repeated processing.

If this is the initial processing, ensure you have set ‘freshRun = TRUE’ at top of this document to process and save data. This will take some time to run.

data <- processPgDetections(pps, mode='db', id='epacific_bw')
saveRDS(data, 'epacific_study.rds')
# Double check warning messages
print(getWarnings(data)$message)

If you have already run the processing code, ensure you have set ‘freshRun = FALSE’ at top of this document to read in the existing .rds file for downstream processing.

3. Assign species identity according to original PAMguard labels, then relabel for consistency across projects.

data <- setSpecies(data, 'pamguard')
reSpecies <- readRDS('species.rds')
print(reSpecies)
data <- setSpecies(data, 'reassign', value=reSpecies)

4. Filter out unwanted data: (1) subset and rename species for BANTER model and (2) filter to retain only Channel 1.

data <- setSpecies(data, method='reassign',
                   value=data.frame(old=c('ZC', 'MS', 'BB', 'BWC', 'BW37V'), new=c("Cuviers", "Stejnegers", "Bairds", "CrossSeamount", "Hubbs")))
goodSpecies<- c("Cuviers", "Stejnegers", "Bairds", "CrossSeamount", "Hubbs", "BW", "BW43", "possBW")
data <- filter(data, species %in% goodSpecies)
data_ch1only <- filter(data, Channel == '1')

5. Calculate Inter-Click Interval (ICI).

data_ch1only <- calculateICI(data_ch1only, time='peakTime')

6. Add GPS data: Add GPS data from PAMGuard table (2 hour threshold), then filter out data without GPS

data_ch1only <- addGps(data_ch1only, thresh = 7200)#11 events over the threshold
getWarnings(data_ch1only)
  
data_ch1only <- filter(data_ch1only, !is.na(Latitude))#filter out events w/o GPS
clicks <- getClickData(data_ch1only)
sum(is.na(clicks$Latitude))
unique(clicks[is.na(clicks$Latitude), 'eventId'])#confirm there are no remaining events w/o GPS

7. Add Environmental Data

#SST
data_ch1only <- matchEnvData(data_ch1only, nc='jplMURSST41mday', var='sst')
#Seafloor Depth
data_ch1only <- matchEnvData(data_ch1only, nc='erdSrtm30plusSeafloorGradient', var='sea_floor_depth')
#Seafloor Gradient
data_ch1only <- matchEnvData(data_ch1only, nc='erdSrtm30plusSeafloorGradient', var='magnitude_gradient')

7. Export Event Wav Files Wav files for events will be exported to a local drive, and then uploaded to Figshare.

#This should only be run one time, with access to original wav files. Will be commented out to ensure it is not accidentally run
#data <- readRDS(here('epacific', 'epacific_study.rds'))
#Check that all events are <120s
#checkStudy(data, maxLength = 120)

#Identify location of all recording files and log files
#data<- addRecordings (data, log = TRUE )
#saveRDS(data, 'epacific_study.rds')

#Identify directory to save wav files, and write event Wav Clips
#wavDir <- here('epacific', 'ccesEventWav')#identify directory to write clips to
#writeEventClips(data, buffer = 5, mode = 'event', useSample = FALSE, outDir = wavDir)
#saveRDS(data, 'epacific_study.rds')

8. Export to Annomate Export annotation information to a csv for upload to Ann-O-Mate. Run code in annomate.R, but do not share this code publicly.

9. Export data for BANTER (and drop species codes that will not be used for training). We will create two datasets: one with ICI and one without ICI, and save these for import into

banter_data <- export_banter(data_ch1only, dropSpecies = c('BW', 'possBW'), 
                         dropVars = c('All_ici', 'Click_Detector_3_ici', 'sea_floor_depth_mean', 'sst_mean', 'productivity_mean', 'chlorophyll_mean',  'magnitude_gradient_mean', 'Latitude', 'Longitude', 'gpsUncertainty'), training=TRUE)
saveRDS(banter_data, file='epacific_banter_data.rds')

banter_data_ici <- export_banter(data_ch1only, dropSpecies = c("BW", "possBW"), dropVars = c('sea_floor_depth_mean', 'sst_mean', 'productivity_mean', 'chlorophyll_mean',  'magnitude_gradient_mean','Latitude', 'Longitude', 'gpsUncertainty'),training=TRUE)
saveRDS(banter_data_ici, file='epacific_banter_data_ici.rds')

banter_data_env <- export_banter(data_ch1only, dropSpecies = c("BW", "possBW"), dropVars = c('Latitude', 'Longitude', 'gpsUncertainty'), training=TRUE)
saveRDS(banter_data_env, file='epacific_banter_data_env.rds')

#save update of Acoustic Study
saveRDS(data_ch1only, 'epacific_study.rds')
saveRDS(data, 'epacificALL_study.rds')

Build a BANTER Classification Model

EC (only) Model

Initialize, Run & Evaluate Detector Model (stage 1).

banter_model_ec <- initBanterModel(banter_data$events)
banter_model_ec <- addBanterDetector(banter_model_ec, banter_data$detectors, ntree=1e4, sampsize=4, importance = TRUE)

plotDetectorTrace(banter_model_ec, detector = paste0('Click_Detector_', 1:3))
plotDetectorTrace(banter_model_ec, detector = paste0('Click_Detector_', 4:6))
summary(banter_model_ec)

Run BANTER Event Model (stage 2)

banter_model_ec <- runBanterModel(banter_model_ec, ntree=1e4, sampsize=4)
summary(banter_model_ec)

Once a stable model is identified, save model with tree/sampsize info in the filename.

saveRDS(banter_model_ec, 'epacific_banter_model_ec_t1e4s4_t1e4s4.rds')

ICI Model

Initialize, Run & Evaluate Detector Model (stage 1).

banter_model_ici <- initBanterModel(banter_data_ici$events)
banter_model_ici <- addBanterDetector(banter_model_ici, banter_data_ici$detectors, ntree=1e4, sampsize=3, importance = TRUE)

plotDetectorTrace(banter_model_ici, detector = paste0('Click_Detector_', 1:3))
plotDetectorTrace(banter_model_ici, detector = paste0('Click_Detector_', 4:6))
summary(banter_model_ici)

Run Event Model (stage 2)

banter_model_ici <- runBanterModel(banter_model_ici, ntree=1e4, sampsize=4)
summary(banter_model_ici)

Once a stable model is identified, save model with tree/sampsize info in the filename.

saveRDS(banter_model_ici, 'epacific_banter_model_ici_t1e4s3_t1e4s4.rds')

ENV Model

Initialize, Run & Evaluate Detector Model (stage 1).

banter_model_env <- initBanterModel(banter_data_env$events)
banter_model_env <- addBanterDetector(banter_model_env, banter_data_env$detectors, ntree=1e4, sampsize=1, importance = TRUE)

plotDetectorTrace(banter_model_env, detector = paste0('Click_Detector_', 1:3))
plotDetectorTrace(banter_model_env, detector = paste0('Click_Detector_', 4:6))
summary(banter_model_env)

Run Event Model (stage 2)

banter_model_env <- runBanterModel(banter_model_env, ntree=1e4, sampsize=5)
summary(banter_model_env)

Once a stable model is identified, save model with tree/sampsize info in the filename.

saveRDS(banter_model_env, 'epacific_banter_model_env_t1e4s1_t1e4s5.rds')

BANTER Analytics

There are a number of visualizations/data products that allow us to visualize our BANTER classifier; most use the rfPermute package (see BANTER Guidelines for more information.

First, load the models (if not a fresh run)

Identify the model you would like to examine (comment out the model you do not want to examine).

model_ec <- banter_model_ec
modelname_ec <- "banter_model_ec"

model_ici <- banter_model_ici
modelname_ici <- "banter_model_ici"

model_env <- banter_model_env
modelname_env <- "banter_model_env"

Extract the Random Forest model object from our BANTER model for analysis.

banter_model_ec_RF <- getBanterModel(model_ec)
banter_model_ici_RF <- getBanterModel(model_ici)
banter_model_env_RF <- getBanterModel(model_env)

Class Priors (Expected Error Rate)

epacific_ec_priors <- classPriors(banter_model_ec_RF, NULL)[,1]
epacific_ici_priors <- classPriors(banter_model_ici_RF, NULL)[,1]
epacific_env_priors <- classPriors(banter_model_env_RF, NULL)[,1]

Confusion Matrix

epacific_ec_confuseMatrix <- rfPermute::confusionMatrix(banter_model_ec_RF)
epacific_ec_confuseMatrix <- cbind(epacific_ec_confuseMatrix, priors = epacific_ec_priors)
epacific_ec_confuseMatrix <- kable(epacific_ec_confuseMatrix, align = "c", digits = c(0,0,0,0,2,2,2))%>%
  kable_classic()%>%
   column_spec(7, border_right = TRUE)%>%
   row_spec(0, bold = TRUE)%>%
   row_spec(4,hline_after = TRUE)%>%
   row_spec(7, bold = TRUE)%>%
   save_kable("../manuscript/manuscript_files/epacific_ec_confuseMatrix.png", zoom = 9)

epacific_ici_confuseMatrix <- rfPermute::confusionMatrix(banter_model_ici_RF)
epacific_ici_confuseMatrix <- cbind(epacific_ici_confuseMatrix, priors = epacific_ici_priors)
epacific_ici_confuseMatrix <- kable(epacific_ici_confuseMatrix, align = "c", digits = c(0,0,0,0,2,2,2))%>%
  kable_classic()%>%
  column_spec(7, border_right = TRUE)%>%
  row_spec(0, bold = TRUE)%>%
  row_spec(4,hline_after = TRUE)%>%
  row_spec(7, bold = TRUE)%>%
  save_kable("../manuscript/manuscript_files/epacific_ici_confuseMatrix.png", zoom = 9)

epacific_env_confuseMatrix <- rfPermute::confusionMatrix(banter_model_env_RF)
epacific_env_confuseMatrix <- cbind(epacific_env_confuseMatrix, priors = epacific_env_priors)
epacific_env_confuseMatrix <- kable(epacific_env_confuseMatrix, align = "c", digits = c(0,0,0,0,2,2,2))%>%
  kable_classic()%>%
  column_spec(7, border_right = TRUE)%>%
  row_spec(0, bold = TRUE)%>%
  row_spec(4,hline_after = TRUE)%>%
  row_spec(7, bold = TRUE)%>%
  save_kable("../manuscript/manuscript_files/epacific_env_confuseMatrix.png", zoom = 9)

BANTER Model E.Pacific EC Confusion Matrix

BANTER Model E.Pacific ICI Confusion Matrix

BANTER Model E.Pacific ENV Confusion Matrix

Proximity Plot

png(('../manuscript/manuscript_files/epacific_ec_proximity.png'), width = 20, height = 20, units = 'cm',  res = 300)
plotProximity(banter_model_ec_RF)
dev.off()
ec_epacific_proximityPlot <- plotProximity(banter_model_ec_RF)

png(('../manuscript/manuscript_files/epacific_ici_proximity.png'), width = 20, height = 20, units = 'cm',  res = 300)
ici_epacific_proximityPlot <- plotProximity(banter_model_ici_RF)
dev.off()
ici_epacific_proximityPlot <- plotProximity(banter_model_ici_RF)

png(('../manuscript/manuscript_files/epacific_env_proximity.png'), width = 20, height = 20, units = 'cm',  res = 300)
env_epacific_proximityPlot <- plotProximity(banter_model_env_RF)

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

dev.off()
env_epacific_proximityPlot <- plotProximity(banter_model_env_RF)

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

Importance Heatmap

png(('../manuscript/manuscript_files/epacific_ec_importance.png'), width = 30, height = 25, units = 'cm',  res = 300)
plotImportance(banter_model_ec_RF, plot.type="heatmap", n=10)
dev.off()
ec_epacific_importance <- plotImportance(banter_model_ec_RF, plot.type="heatmap", n=10)

png(('../manuscript/manuscript_files/epacific_ici_importance.png'), width = 30, height = 25, units = 'cm',  res = 300)
plotImportance(banter_model_ici_RF, plot.type="heatmap", n=10)
dev.off()
ici_epacific_importance <- plotImportance(banter_model_ici_RF, plot.type="heatmap", n=10)

png(('../manuscript/manuscript_files/epacific_env_importance.png'), width = 30, height = 25, units = 'cm',  res = 300)
plotImportance(banter_model_env_RF, plot.type="heatmap", n=10)
dev.off()
env_epacific_importance <- plotImportance(banter_model_env_RF, plot.type="heatmap", n=10)

PlotVotes

png(('../manuscript/manuscript_files/epacific_ec_votes.png'), width = 20, height = 20, units = 'cm',  res = 300)
plotVotes(banter_model_ec_RF)
dev.off()
epacific_votes <- plotVotes(banter_model_ec_RF)

png(('../manuscript/manuscript_files/epacific_ici_votes.png'), width = 20, height = 20, units = 'cm',  res = 300)
plotVotes(banter_model_ici_RF)
dev.off()
ici_epacific_votes <- plotVotes(banter_model_ici_RF)

png(('../manuscript/manuscript_files/epacific_env_votes.png'), width = 20, height = 20, units = 'cm',  res = 300)
plotVotes(banter_model_env_RF)
dev.off()
env_epacific_votes <- plotVotes(banter_model_env_RF)

Plot Predicted Probabilities

plotPredictedProbs(banter_model_ec_RF, bins=30, plot=TRUE)

plotPredictedProbs(banter_model_ici_RF, bins=30, plot=TRUE)

plotPredictedProbs(banter_model_env_RF, bins=30, plot=TRUE)

Create Figure for Publication

confuse <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ec_confuseMatrix.png'))%>%
  image_border(color="#ffffff", geometry = "50x130")%>%
  image_annotate("a) Confusion Matrix", size=300, color = "black")
vote <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ec_votes.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_annotate("d) Vote Plot", size=300, color = "black")
prox <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ec_proximity.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_annotate("b) Proximity Plot", size=300, color = "black")
heat <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ec_importance.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_scale("3300")%>%
  image_annotate("d) Importance Heat Map", size=300, color = "black")
  
epacific_ec_Figure <-image_append(c(prox, heat, vote))
epacific_ec_Figure<- image_append(c(confuse, epacific_ec_Figure), stack=TRUE)
image_write(epacific_ec_Figure, path = here('manuscript', 'manuscript_files','epacific_ec_Figure.png'), format ='png')
print(epacific_ec_Figure, info=FALSE)

confuse <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ici_confuseMatrix.png'))%>%
  image_border(color="#ffffff", geometry = "50x130")%>%
  image_annotate("a) Confusion Matrix", size=300, color = "black")
vote <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ici_votes.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_annotate("d) Vote Plot", size=300, color = "black")
prox <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ici_proximity.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_annotate("b) Proximity Plot", size=300, color = "black")
heat <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_ici_importance.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_scale("3300")%>%
  image_annotate("d) Importance Heat Map", size=300, color = "black")
  
epacific_ici_Figure <-image_append(c(prox, heat, vote))
epacific_ici_Figure<- image_append(c(confuse, epacific_ici_Figure), stack=TRUE)
image_write(epacific_ici_Figure, path = here('manuscript', 'manuscript_files','epacific_ici_Figure.png'), format ='png')
print(epacific_ici_Figure, info=FALSE)

confuse <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_env_confuseMatrix.png'))%>%
  image_border(color="#ffffff", geometry = "50x130")%>%
  image_annotate("a) Confusion Matrix", size=300, color = "black")
vote <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_env_votes.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_annotate("d) Vote Plot", size=300, color = "black")
prox <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_env_proximity.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_annotate("b) Proximity Plot", size=300, color = "black")
heat <- magick::image_read(here('manuscript', 'manuscript_files', 'epacific_env_importance.png'))%>%
  image_border(color="#ffffff", geometry = "270x130")%>%
  image_scale("3300")%>%
  image_annotate("d) Importance Heat Map", size=300, color = "black")
  
epacific_env_Figure <-image_append(c(prox, heat, vote))
epacific_env_Figure<- image_append(c(confuse, epacific_env_Figure), stack=TRUE)
image_write(epacific_env_Figure, path = here('manuscript', 'manuscript_files','epacific_env_Figure.png'), format ='png')
print(epacific_env_Figure, info=FALSE)